Updating Models and Their Uncertainties. I: Bayesian Statistical Framework

Abstract

The problem of updating a structural model and its associated uncertainties by utilizing dynamic response data is addressed using a Bayesian statistical framework that can handle the inherent ill-conditioning and possible nonuniqueness in model updating applications. The objective is not only to give more accurate response predictions for prescribed dynamic loadings but also to provide a quantitative assessment of this accuracy. In the methodology presented, the updated (optimal) models within a chosen class of structural models are the most probable based on the structural data if all the models are equally plausible a priori. The prediction accuracy of the optimal structural models is given by also updating probability models for the prediction error. The precision of the parameter estimates of the optimal structural models, as well as the precision of the optimal prediction-error parameters, can be examined. A large-sample asymptotic expression is given for the updated predictive probability distribution of the uncertain structural response, which is a weighted average of the prediction probability distributions for each optimal model. This predictive distribution can be used to make model predictions despite possible nonuniqueness in the optimal models.

Additional Information

©ASCE The manuscript for this paper was submitted for review and possible publication on May 10, 1996. This paper was completed while the first writer was on sabbatical leave at the Hong Kong University of Science and Technology. The hospitality of Jay-Chung Chen, Director of the Research Centre, is greatly appreciated.

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